Hybrid Optimization Technique Research Articles

Innovative Hierarchical Control of Multiple Microgrids: Cheetah Meets PSO

The expansion of microgrids (MGs) comes from the increasing integration of renewable energy resources (RES) and energy storage systems (ESs) into distribution networks. However, effective integration, coordination, and control of Multiple Microgrids (MMGs) during energy transition from microgrid to grid, and vice versa presents a formidable challenge. This arises from the necessity to ensure smooth operation, coordination, and control of MMGs for stability and efficiency during transitions. The dynamic operation of MMGs due to intermittent renewable energy in microgrids and load variations presents an additional challenge for hierarchical control techniques.This paper provides an Artificial Intelligent (AI)-based control technique and introduces an innovative hybrid optimization technique, denoted as HYCHOPSO, derived from the fusion of Cheetah Optimization (CHO) and Particle Swarm Optimization (PSO) techniques, and the quantitative findings of extensive benchmark testing, constitute key aspects of novelty. The strategic choice of this novel optimization technique, coupled with a Proportional-Integral (PI) controller, provides key insights.HYCHOPSO is tested within benchmark functions and reaches its optimal score before 50 iterations as compared with optimization algorithms namely CHO, GWO, PSO, Hybrid-GWO-PSO, and SSIA-PSO which reaches after approximately 200 iterations. HYCHOPSO consistently exhibits the lowest mean values, indicating its robust convergence capabilities.The proposed control technique results in a significant reduction of the microgrid's Current Total Demand Distortion to 1.1%, meeting acceptable limits for a 600V-bus According to IEEE519-2014 standard, even under nonlinear loads. Additionally, precise regulation of voltage and frequency is achieved with0.19%−+ deviations and a frequency overshoot of 1.9%. Furthermore, optimization contributes to seamless energy transition from microgrid to grid, achieving synchronization in less than 5 milliseconds. This enhances the real application's reliability, flexibility, scalability, and robustness. Furthermore, HYCHOPSO is introduced as an AI-based technique to facilitate control parameter design under dynamic conditions, with substantial simulation cases demonstrating control feasibility

Demand side management strategy for smart building using multi-objective hybrid optimization technique

This study proposes a home energy management system that uses the load-shifting technique for demand-side management as a way to improve the energy consumption patterns of a smart house. This system's goal is to optimize the energy of household appliances in order to effectively regulate load demand, with the end result being a reduction in the peak-to-average ratio (PAR) and a consequent minimization of electricity costs. This is accomplished while also keeping user comfort as a priority. Load scheduling based on both a next-day and real-time basis is what is used to meet the load demand requested by energy customers. In addition to providing a fitness criterion, utilizing a multi-objective hybrid optimization technique makes it easier to achieve an equitable distribution of workload between on-peak and off-peak hours. Moreover, the idea of developing coordination among home appliances in order to achieve real-time rescheduling is now being studied as a concept. Because of the inherent parallels between the two problems, the real-time rescheduling issue is framed as a knapsack problem and is solved using a dynamic programming strategy. The performance of the suggested methodology is evaluated in this study in relation to real-time pricing (RTP), time-of-use pricing (ToU), and crucial peak pricing (CPP). The simulation findings, which were assessed using a confidence interval that was set at 95 %, provide proof of the relevance that has been shown to be associated with the proposed optimization method. During scheduling RTP signal showcases a minimum PAR of 2.22 and a cost reduction of 24.06 % for HAG compared to the unscheduled case. Under the TOU tariff, HAG manages to reduce PAR by 46.14 % and cost by 20.44 %. Similarly, in the case of CPP, HAG outperforms by reducing PAR by up to 29.5 % and cost by up to 31.47 %.

A bizarre synthesized cascaded optimized predictor (BizSCOP) model for enhancing security in cloud systems

Due to growing network data dissemination in cloud, the elasticity, pay as you go options, globally accessible facilities, and security of networks have become increasingly important in today's world. Cloud service providers, including AWS, Azure, GCP, and others, facilitate worldwide expansion within minutes by offering decentralized communication network functions, hence providing security to cloud is still remains a challenging task. This paper aims to introduce and evaluate the Biz-SCOP model, a novel intrusion detection system developed for cloud security. The research addresses the pressing need for effective intrusion detection in cloud environments by combining hybrid optimization techniques and advanced deep learning methodologies. The study employs prominent intrusion datasets, including CSE-CIC-IDS 2018, CIC-IDS 2017, and a cloud intrusion dataset, to assess the proposed model's performance. The study's design involves implementing the Biz-SCOP model using Matlab 2019 software on a Windows 10 OS platform, utilizing 8 GB RAM and an Intel core i3 processor. The hybrid optimization approach, termed HyPSM, is employed for feature selection, enhancing the model's efficiency. Additionally, an intelligent deep learning model, C2AE, is introduced to discern friendly and hostile communication, contributing to accurate intrusion detection. Key findings indicate that the Biz-SCOP model outperforms existing intrusion detection systems, achieving notable accuracy (99.8%), precision (99.7%), F1-score (99.8%), and GEO (99.9%). The model excels in identifying various attack types, as demonstrated by robust ROC analysis. Interpretations and conclusions emphasize the significance of hybrid optimization and advanced deep learning techniques in enhancing intrusion detection system performance. The proposed model exhibits lower computational load, reduced false positives, ease of implementation, and improved accuracy, positioning it as a promising solution for cloud security.

Hybrid cheetah particle swarm optimization based optimal hierarchical control of multiple microgrids

The emergence of microgrids arises from the growing integration of Renewable Energy Resources (RES) and Energy Storage Systems (ESSs) into Distribution Networks (DNs). Effective integration, coordination, and control of Multiple Microgrids (MMGs) whereas navigating the complexities of energy transition within this context poses a significant challenge. The dynamic operation of MMGs is a challenge faced by the traditional distributed hierarchical control techniques. The application of Artificial Intelligence (AI) techniques is a promising way to improve the control and dynamic operation of MMGs in future smart DNs. In this paper, an innovative hybrid optimization technique that originates from Cheetah Optimization (CHO) and Particle Swarm Optimization (PSO) techniques is proposed, known as HYCHOPSO. Extensive benchmark testing validates HYCHOPSO’s superiority over CHO and PSO in terms of convergence performance. The objective for this hybridization stems from the complementary strengths of CHO and PSO. CHO demonstrates rapid convergence in local search spaces, while PSO excels in global exploration. By combining these techniques, the aim is to leverage their respective advantages and enhance the algorithm's overall performance in addressing complex optimization problems. The contribution of this paper offering a unique approach to addressing optimization challenges in microgrid systems. Through a comprehensive comparative study, HYCHOPSO is evaluated against various metaheuristic optimization approaches, demonstrating superior performance, particularly in optimizing the design parameters of Proportional-Integral (PI) controllers for hierarchical control systems within microgrids. This contribution expands the repertoire of available optimization methodologies and offers practical solutions to critical challenges in microgrid optimization, enhancing the efficiency, reliability, and sustainability of microgrid operations. HYCHOPSO achieves its optimal score within fewer than 50 iterations, unlike CHO, GWO, PSO, Hybrid-GWO-PSO, and SSIA-PSO, which stabilize after around 200 iterations. Across various benchmark functions, HYCHOPSO consistently demonstrates the lowest mean values, attains scores closer to the optimal values of the benchmark functions, underscoring its robust convergence capabilities.the proposed HYCHOPSO algorithm, paired with a PI controller for distributed hierarchical control, minimizes errors and enhances system reliability during dynamic MMG operations. Using HYCHOPSO framework, an accurate power sharing, voltage/frequency stability, seamless grid-to-island transition, and smooth resynchronization are achieved. This enhances the real application's reliability, flexibility, scalability and robustness.

Design of experiments coupled with Bayesian optimisation for nanolubricant formulation

Nanoparticle-enhanced lubricants (nanolubricants) achieve tribological properties that can signficantly outperform the unmodified base oil. However, this performance enhancement is known to be extremely senstive to the concentration of nanoparticles used. Here, we introduce a new approach to optimizing nanolubricants combining design of experiments (DOE) with machine learning, addressing the challenge of determining optimal nanoparticle concentration in base oils and demonstrating significant performance improvements. Combining Box-Behnken design with Bayesian optimization, we identify an optimal concentration of 0.04 wt% copper oxide (CuO) nanoparticles in a standard engine oil (SAE 20W50), resulting in a 42% reduction in coefficient of friction (COF) relative to the base oil. Surface analysis (AFM, SEM) confirmed improvement of anti-wear and anti-friction characteristics. Signficant enhacements of thermophysical properties were observed (demonstrated by increased flash and pour points, by 10% and 7% respectively), signifying greater thermal stability. The dynamic viscosity remained consistent, with a beneficial reduction observed under conditions of high temperature and shear, which aligns with the demands of engine lubricant standards for PC-12B oils, with a reduction from 3.49 to 3.18 cP (8%). This research introduces a novel hybrid optimization technique that provides a versatile framework for optimizing nanofluids, addressing the critical challenge of determining optimal nanoparticle concentration.

Integration of Social Network Analysis in the Evaluation of Advertising Communication Effectiveness and Legal Compliance

A fuzzy genetic algorithm (FGA) is a hybrid optimization technique that combines the principles of fuzzy logic with genetic algorithms (GAs). Both fuzzy logic and genetic algorithms are powerful tools for optimization and decision-making in complex, uncertain environments. A Fuzzy Genetic Algorithm (FGA) can be a valuable approach for Social Network Analysis (SNA), where the complexity of social interactions often involves uncertainty and imprecision. This paper proposes an innovative approach to evaluate advertising communication effectiveness and legal compliance by integrating Social Network Analysis (SNA) with Sugeno Fuzzy Genetic Network Analysis (SF-GNA). By combining SNA's insights into communication dynamics and SF-GNA's ability to model complex relationships, the proposed framework offers a comprehensive assessment of advertising campaigns. The integration of SNA enables the identification of influential nodes and communication pathways within social networks, shedding light on the dissemination and reception of advertising messages. Concurrently, SF-GNA facilitates the analysis of legal compliance by assessing the fuzzy relationships between advertising content and regulatory requirements. Social Network Analysis identified key influencers within the network, with nodes A, B, and C demonstrating high centrality scores of 0.85, 0.79, and 0.76, respectively. SF-GNA evaluated the effectiveness of advertising messages, with a membership value of 0.92 indicating strong alignment with consumer preferences and engagement. SF-GNA analyzed the compliance of advertising content with regulatory standards, yielding a compliance score of 0.88, indicating adherence to legal requirements. With Specific regulatory aspects, such as truthfulness and data privacy, were evaluated with membership values of 0.90 and 0.85, respectively, indicating robust compliance.

Smart Circuit Design Machine Learning-Driven Optimization for Enhanced Performance in Electronics and Computer Engineering.

In the realm of Electronics and Computer engineering, achieving optimal performance of circuits amidst escalating complexity poses significant challenges. Traditional manual optimization techniques are often inadequate to navigate the intricacies of modern electronic systems. This paper advocates for the adoption of machine learning-driven optimization as a transformative approach to smart circuit design. By leveraging machine learning algorithms, engineers can systematically explore the expansive design space, discern complex relationships between circuit parameters and performance metrics, and ultimately enhance the efficiency and effectiveness of electronic circuits. This paper comprehensively reviews the application of machine learning techniques in circuit design optimization. Supervised learning algorithms such as neural networks, support vector machines, and decision trees enable the modeling of intricate interdependencies within electronic circuits. Unsupervised learning techniques, including clustering and dimensionality reduction, facilitate efficient exploration of the design landscape by identifying patterns and correlations. Additionally, reinforcement learning algorithms offer an autonomous approach to circuit optimization through iterative learning and refinement. Real-world applications of machine learning-driven optimization in electronics and computer engineering span various domains, including power-efficient integrated circuits, signal processing algorithm optimization, and layout optimization for enhanced performance and reliability. Moreover, machine learning techniques play a crucial role in mitigating variability in semiconductor manufacturing processes, ensuring robustness and reliability of electronic systems in the face of uncertainties. Despite the promising potential of machine learning in circuit design optimization, challenges such as dataset acquisition, model interpretability, and scalability to complex circuits persist. Addressing these challenges requires innovative research endeavors, including the development of hybrid optimization techniques and novel hardware architectures. DOI: https://doi.org/10.52783/tjjpt.v45.i02.6339

Optimisation-based strategies to identification of material parameters of hygro-thermo-mechanical problems

Applications involving hygro-thermo-mechanical models are found in engineering fields as diverse as electronic packaging, concrete structures, composite materials and wood structures. Within the framework of wood processing, hygro-thermo-mechanical problems account for the cross-dependence of moisture content, temperature and dimensional changes of wooden components. Numerical simulation plays an important role in predicting the behaviour of wooden structures and, therefore, use of appropriate process and material parameters is essential for a successful prediction. The present work is inserted in this context and addresses application of optimisation techniques to identification of heat transfer, moisture diffusion and moisture-dependent swelling/shrinkage parameters. The direct problem is formulated based on a fully coupled transient solution of the energy, moisture and momentum transfer conservation laws. A global–local hybrid optimisation technique is proposed combining swarm intelligence and deterministic approaches, respectively based on particle swarm optimisation and the Nelder–Mead (NM) technique. The first stage aims at reducing the search space, so that the NM algorithm is able to determine the global minimum with acceptable accuracy. The strategy is both robust and efficient, being able to avoid local minima with reduced number of fitness computations. An illustrative example featuring a moisture-gaining process is also discussed, in which special attention is placed on the elastic and material swelling coupled effects caused by moisture transfer.

Optimizing Software Defect Prediction Models: Integrating Hybrid Grey Wolf and Particle Swarm Optimization for Enhanced Feature Selection with Popular Gradient Boosting Algorithm

Software defects, also referred to as software bugs, are anomalies or flaws in computer program that cause software to behave unexpectedly or produce incorrect results. These defects can manifest in various forms, including coding errors, design flaws, and logic mistakes, this defect have the potential to emerge at any stage of the software development lifecycle. Traditional prediction models usually have lower prediction performance. To address this issue, this paper proposes a novel prediction model using Hybrid Grey Wolf Optimizer and Particle Swarm Optimization (HGWOPSO). This research aims to determine whether the Hybrid Grey Wolf and Particle Swarm Optimization model could potentially improve the effectiveness of software defect prediction compared to base PSO and GWO algorithms without hybridization. Furthermore, this study aims to determine the effectiveness of different Gradient Boosting Algorithm classification algorithms when combined with HGWOPSO feature selection in predicting software defects. The study utilizes 13 NASA MDP dataset. These dataset are divided into testing and training data using 10-fold cross-validation. After data is divided, SMOTE technique is employed in training data. This technique generates synthetic samples to balance the dataset, ensuring better performance of the predictive model. Subsequently feature selection is conducted using HGWOPSO Algorithm. Each subset of the NASA MDP dataset will be processed by three boosting classification algorithms namely XGBoost, LightGBM, and CatBoost. Performance evaluation is based on the Area under the ROC Curve (AUC) value. Average AUC values yielded by HGWOPSO XGBoost, HGWOPSO LightGBM, and HGWOPSO CatBoost are 0.891, 0.881, and 0.894, respectively. Results of this study indicated that utilizing the HGWOPSO algorithm improved AUC performance compared to the base GWO and PSO algorithms. Specifically, HGWOPSO CatBoost achieved the highest AUC of 0.894. This represents a 6.5% increase in AUC with a significance value of 0.00552 compared to PSO CatBoost, and a 6.3% AUC increase with a significance value of 0.00148 compared to GWO CatBoost. This study demonstrated that HGWOPSO significantly improves the performance of software defect prediction. The implication of this research is to enhance software defect prediction models by incorporating hybrid optimization techniques and combining them with gradient boosting algorithms, which can potentially identify and address defects more accurately

Dynamic Spectrum Sensing For 5G Cognitive Radio Networks Using Optimization Technique

With increasing development of 5G technology, the rapid growth of various technologies and the growth of various wireless devices, demand for wireless spectrum becomes more urgent. Wireless communication technologies have been advancing rapidly, leading to the emergence of 5G communication systems .Spectrum sensing is the key model utilized to access the spectrum dynamically in CRN. Various researchers are done in spectrum sensing scenario and different methods are designed to perform the task of spectrum resource sharing. Most of the methods design a decision statistics for identifying the signal by analyzing the features of noise and signals. One promising approach to address the challenges of spectrum sensing in cognitive radio networks for 5G communication is the integration of deep learning with hybrid optimization techniques. By combining the power of deep learning algorithms with optimization methods, it becomes possible to improve the efficiency and accuracy of spectrum sensing in dynamic and diverse communication scenarios. One of the key advantages of MIMO-based spectrum sensing is its ability to exploit the spatial diversity inherent in the environment. By using multiple antennas at both the transmitter and receiver, MIMO systems can distinguish between signals arriving from different directions, thereby improving the accuracy of spectrum sensing. Moreover, MIMO technology also enables the cognitive radio network to utilize the available spectrum more efficiently. With the ability to establish multiple parallel communication links, MIMO-based cognitive radio networks can achieve higher data rates and improved spectral efficiency, especially in dynamic and challenging radio environments. Research in this field is focused on further enhancing the performance of MIMO-based spectrum sensing in cognitive radio networks through advanced signal processing algorithms and machine learning technique Spectrum sensing detect existence of primary users (PUs) and it becomes a main research topic of CRN in industry and academic domain. This research developed a new framework based on algorithm to progress the mechanism of spectrum sensing in the CRN by detecting the availability of free channel. The signal components are extracted from the received signal and thereby spectrum availability of detected through fusion center using proposed Feedback Artificial Optimization Algorithm-based Deep Q network. However, Simulation results show that the proposed multiple-input-multiple-output(MIMO) spectrum sensing method achieves good performance Cognitive Radion Network attains maximum probability of detection and minimum probability of false alarm as 70%, and 38% for Rayleigh channel.

A Novel Framework of Hybrid Optimization Techniques for Contrast-Enhancement in Cardiac MRI Medical Images

Medical image processing plays a crucial role in diagnosing and treating various diseases, with Magnetic Resonance Imaging (MRI) being a widely used modality. A previous study introduced an algorithm aimed at eliminating noise from MRI images through a filtering process, effectively suppressing mixed noise while preserving important structures like edges. However, limitations in optimizing parameters for image enhancement and occasional edge preservation failures were observed. Addressing the challenge of inadequate contrast enhancement in medical image processing, this study proposes a solution utilizing the hybrid cetacean optimization algorithm and Sand Cat Swarm Optimization (COA-SCSO) to enhance contrast in MRI images. The proposed approach focuses on improving the contrast nature in MRI images to mitigate image degradation during acquisition. Cardiac MRI images from SCMR consensus and AMRG Atlas databases are analyzed, emphasizing the consideration of spatial information for enhancement. The enhancement process involves integrating a cost function with a contrast measure based on multiple metrics to create a novel transformation function. Soft-computing approaches are employed to reduce time complexity, while expanding the search pattern through a transformation function in the spatial domain. This method enhances pixel intensity, contributing to increased image resolution. Performance evaluation of the proposed algorithm incorporates various parameters such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Normalized Absolute Error (NAE), and Structural Similarity Index (SSIM). Results from analysis of ten cardiac MRI images indicate that the proposed algorithm achieves the highest PSNR (98) and SSIM (0.99) values, as well as the lowest NAE (-0.17) and MSE (0.16) values, compared to the particle swarm optimized texture-based histogram equalization (PSOTHE) method and modified sunflower optimization (MSFO) method. Overall, the COA-SCSO approach demonstrates promising results in enhancing contrast in MRI images, offering potential benefits for medical diagnosis and treatment planning. Further research could explore additional optimization techniques and validation on larger datasets to further validate its effectiveness in clinical practice.

Optimal allocation of controllable power factor distributed generation with network reconfiguration on electric vehicle charging station loaded distribution network

This paper presents a hybrid optimisation technique to solve the problem of optimal allocation of controllable power factor Distributed Generators (DGs) together with network reconfiguration on Electric Vehicle Charging Stations (EVCSs)-loaded distribution networks. The suggested hybrid optimisation strategy combines the Artificial Gorilla Troops Optimizer (GTO) algorithm with genetic operators to improve convergence characteristics and prevent premature convergence to local optima. The objective is to minimise power loss, bus voltage deviation and voltage stability index reduction, which are formulated as a multi-objective function. The effectiveness of the proposed approach is evaluated on 69 and 118-bus distribution networks. Additionally, a comparative analysis is performed to evaluate the efficacy of the suggested approach in comparison with standard GTO and other well-known optimisation algorithms. The results demonstrate that simultaneous optimal placement of controllable power factor DGs with network reconfiguration demonstrates superior effectiveness in mitigating the adverse impact of EVCSs on distribution networks compared to optimisation of fixed-power factor DG placement with network reconfiguration. Furthermore, the hybrid optimisation approach outperforms other applied algorithms in terms of solution quality.

Dynamic Load Balancing in Cloud Computing: Optimized RL-Based Clustering with Multi-Objective Optimized Task Scheduling

Dynamic load balancing in cloud computing is crucial for efficiently distributing workloads across available resources, ensuring optimal performance. This research introduces a novel dynamic load-balancing approach that leverages a deep learning model combining Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to calculate load values for each virtual machine (VM). The methodology aims to enhance cloud performance by optimizing task scheduling and stress distribution. The proposed model employs a dynamic clustering mechanism based on computed loads to categorize VMs into overloaded and underloaded clusters. To improve clustering efficiency, the approach integrates Reinforcement Learning (RL) with a sophisticated Hybrid Lyrebird Falcon Optimization (HLFO) algorithm. HLFO merges the Lyrebird Optimization Algorithm (LOA) and Falcon Optimization Algorithm (FOA), enhancing the effectiveness of load balancing. A Multi-Objective Hybrid Optimization model is introduced to optimize task scheduling while considering Quality of Service (QoS) parameters, including makespan minimization, energy consumption reduction, balanced CPU utilization, efficient memory usage, and task prioritization. The implementation, conducted in Python and CloudSim, demonstrates the model’s ability to effectively allocate work between virtual machines (VMs) and physical machines (PMs), resulting in improved resource utilization, shortened makespan, enhanced CPU usage, and rigorous assessments affirming its efficacy. This research addresses the complexity of dynamic load balancing in cloud environments by combining deep learning, reinforcement learning, and hybrid optimization techniques, offering a comprehensive solution to optimize cloud performance under varying workloads and resource conditions.

Hybrid Technologies for Water Heating Applications: A Review

The over-reliance on fossil resources necessitates the development of a sustainable energy system. Renewable energy and efficient hybrid water heating technologies are viable net-zero energy options. The economic benefits of these hybrid technologies offer a promising prospect for widespread adoption in developing countries as a means of increasing the hot water production. These hybrid technologies are becoming increasingly popular for domestic thermal applications in remote areas to compensate for energy shortages. This paper provides an overview of hybrid renewable water heating technologies with a focus on hybrid configurations, optimization techniques, mono-particle, and hybrid nanofluids and modelling. This paper also highlights the prospects for increasing the economic attractiveness and public acceptance of such systems.

Optimization of temperature distribution in building envelopes of Ahmedabad region using nano-encapsulated phase change material

The incorporation of Phase Change Materials (PCM) into the building envelopes helps to increase indoor thermal comfort while lowering energy usage. The selection of PCM is directly related to its performance and might be viewed as an optimization challenge. The manuscript highlights the optimization of temperature distribution in building envelopes (walls and roofs) of Ahmedabad by choosing the suitable PCM and Nano-particle. Three different PCMs (RT31, RT 35, and RT42) and Nano-particles (Al2O3, CuO, and ZnO) with different concentrations (12 %, 16 %, and 20 %) are considered for the optimization. The PCMs are encapsulated into the building layer thickness to examine their characteristics like effective thermal conductivity, concentration, melting point, etc. These parameters are evaluated in a room that measures 5 m x 4 m x 3.5 m (L x W x H). The design-builder software (Version 7.0.2.006) is used to carry out the steady-state numerical simulations for this building envelope. The results suggest that the integration of PCM reduces the building's overall energy consumption by 7.92 kWh/m2 per year. The optimal design is proposed by establishing a temperature difference (ΔT) in building envelopes and utilizing a hybrid optimization technique (a combination of Artificial neural network (ANN) and Genetic algorithm (GA)). This temperature difference of the building envelope is confirmed to be dependent on their PCM melting point, effective thermal conductivity, concentration (in%), and PCM layer thickness. Hybrid optimization is found to be the most stable strategy for predicting temperature dilation and energy storage in building envelopes. Thus this paper gives a systematic understanding of the selection of PCM and Nano-particles and identifies the suitable PCM layer thickness for encapsulation.

A novel technique for implementing hybrid optimization technique for PV thermal images to categorize and localize the faults

In order to identify and locate flaws in solar thermal images, this research suggests using an optimization-tuned CNN classifier. The input thermal images are initially pre-processed to remove the noise present in them. After pre-processing, features like LBP, LDP, and LOOP are extracted. The collected features are then combined to produce a feature vector, which is the input to the proposed CNN classifier. Single hotspots, multiple hotspots, and string hotspots are the three types of faults that are supposed to be classified. After the classification process, the defects are located using the VGG-16 model. The weights of the CNN and VGG-16 models are modified using the proposed AqWH algorithm, which includes the distinctive characteristics of the wild horse and the Aquila search agents, to enhance classification and localization accuracy. The suggested possesses accuracy levels of 90% for classification and 96.11% for localization tasks, showing its superiority over conventional methods.

ARSIP: Automated Robotic System for Industrial Painting

This manuscript addresses the critical need for precise paint application to ensure product durability and aesthetics. While manual work carries risks, robotic systems promise accuracy, yet programming diverse product trajectories remains a challenge. This study aims to develop an autonomous system capable of generating paint trajectories based on object geometries for user-defined spraying processes. By emphasizing energy efficiency, process time, and coating thickness on complex surfaces, a hybrid optimization technique enhances overall efficiency. Extensive hardware and software development results in a robust robotic system leveraging the Robot Operating System (ROS). Integrating a low-cost 3D scanner, calibrator, and trajectory optimizer creates an autonomous painting system. Hardware components, including sensors, motors, and actuators, are seamlessly integrated with a Python and ROS-based software framework, enabling the desired automation. A web-based GUI, powered by JavaScript, allows user control over two robots, facilitating trajectory dispatch, 3D scanning, and optimization. Specific nodes manage calibration, validation, process settings, and real-time video feeds. The use of open-source software and an ROS ecosystem makes it a good choice for industrial-scale implementation. The results indicate that the proposed system can achieve the desired automation, contingent upon surface geometries, spraying processes, and robot dynamics.

A Hybrid Design of Experiment Approach in Analyzing the Electrical Discharge Machining Influence on Stir Cast Al7075/B4C Metal Matrix Composites

The present work centers on aluminum-based metal matrix composites (AMCs), synthesized via stir casting and then processed by electrical discharge machining (EDM) in the case of Al7075 as a matrix and 6 wt.% boron carbide (B4C) as reinforcement. A design of experiment (DoE) approach, powered by hybrid optimization techniques (such as the entropy weight method (EWM), grey relational analysis (GRA) incorporated Taguchi method) was used to investigate the relationship between current (I), pulse ON time (Ton), pulse OFF time (Toff), and electrode gap (Gap) as input parameters and the material removal rate (MRR), tool wear rate (TWR), and surface roughness (SR) as response parameters. The results showed that an I = 140 A, Ton = 120 ms, Toff = 50 ms, and Gap = 0.4 mm combination gives the best response parameters of MRR = 0.5628 mm3/min, TWR = 0.0048 mm3/min, and SR = 4.4034 μs.

Numerical study of the influence of layers number and their orientation in a CFRP using improved ANN based on Enhanced Jaya

The advancement of technology in the field of artificial intelligence has facilitated significant progress in various areas, particularly in optimizing the structural configuration of multilayer composites. This study's primary objective is to investigate how geometric parameters, such as fiber orientation and the number of layers influence the mechanical properties of these materials. To predict fracture toughness in bending tests, we employed a hybrid optimization technique (E-Jaya-ANN) and compared it with the Jaya-ANN method to assess the improved approach's accuracy. Furthermore, we developed a numerical model based on the Hashin damage criterion using ABAQUS software to anticipate the response of composite specimens (CFRP) when subjected to a bending load. We validated the reliability of the code results by comparing them with experimental data. Subsequently, we generated a series of numerical results, representing various practical scenarios, to serve as the foundation for training an artificial neural network (ANN). This enabled us to obtain an enhanced architecture for the laminate layers.

Transformer hot spot temperature estimation through adaptive neuro fuzzy inference system approach

Transformer performance and efficiency can be enhanced by effectively address the properties of its insulation system. The power transformer insulation system weakens as a result of operational thermal stresses brought on by dynamic loading and shifting environmental patterns. Winding hot spot temperature is a crucial metric that must be maintained below the prescribed limit while power transformers are operating so as to maintained power system reliability. This is due to the fact that, among other variables, the time-dependent aging effect of insulation depends on transitions in hot spot temperatures. Due to the non‐linear nature of the conventional mathematical models used to determine these temperatures, and complexity of thermal phenomena, investigations still need to be exercised to fully understand the variables that associate with hot spot temperature computation with minimum error. This paper explores the possibilities of enhancing top oil and hot spot temperature estimation accuracy through the use of an adaptive neuro-fuzzy inference (ANFIS) technique. The paper presents an adaptive neuro fuzzy model to approximate the hot spot temperature of a mineral oil-filled power transformer based on loading, and established top oil temperature. Initially, a sub-ANFIS top oil temperature estimation model based on loading and ambient temperature as inputs is established. Using a hybrid optimization technique, the ANFIS membership functions were fine-tuned throughout the training process to reduce the difference between the actual and anticipated outcomes. The correctness and reliability of the created adaptive neural fuzzy model have been verified using real-world field data from a 60/90MVA, 132 kV power transformers under dynamic operating regimes. The ANFIS model results were validated against field measured values and literature-based electrical-thermal analogous models, establishing a precise input-output correlation. The developed ANFIS model achieves the highest coefficient of determination for both TOT and HST (0.98 and 0.96) and the lowest mean square error (7.8 and 10.3) among the compared thermal models. Correct determination of HST can help asset managers in thermal analysis trending of the in-service transformers, helping them to make proper loading recommendations for safeguarding the asset.